Papers by Ruotong Liao
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models (2024.naacl-long)
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| Challenge: | Existing methods to forecast links on temporal knowledge graphs are embedding-based . but they face a strong challenge in modeling the unseen zero-shot relations . |
| Approach: | They propose to embed knowledge graphs (TKGF) entities and relations based on observed contexts into embedding-based methods to model unseen zero-shot relations. |
| Outcome: | The proposed methods show strong performance on traditional TKG forecasting benchmarks, but they face a strong challenge in modeling unseen zero-shot relations that have no prior graph context. |
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods for temporal relational forecasting are limited and require limited training data. |
| Approach: | They propose a retrieval-augmented generation framework that uses temporal logical rule-based retrieval and parameter-efficient instruction tuning to solve temporal knowledge forecasting challenges. |
| Outcome: | The proposed framework outperforms conventional methods in the temporal knowledge graph domain with low computation resources. |
VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs (2024.findings-emnlp)
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| Challenge: | Long video understanding presents unique challenges due to the complexity of reasoning over extended timespans. |
| Approach: | They propose a framework VideoINSTA to leverage large language models for video understanding . they propose 'event-based temporalreasoning' and 'content-based spatial reasoning' |
| Outcome: | The proposed model significantly improves state-of-the-art on three long video question-answering benchmarks. |
ECOLA: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations (2023.findings-acl)
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Zhen Han, Ruotong Liao, Jindong Gu, Yao Zhang, Zifeng Ding, Yujia Gu, Heinz Koeppl, Hinrich Schütze, Volker Tresp
| Challenge: | Existing enhancement approaches cannot be applied to temporal knowledge graphs (tKGs) existing enhancement approaches assume knowledge embedding is time-independent, whereas entity embedded in tKG models evolves . |
| Approach: | They propose to use textual data to enhance temporal knowledge embedding by Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations (ECOLA) to evaluate ECOLA, they introduce three new datasets for training and evaluation. |
| Outcome: | The proposed model significantly improves Hits@1 on the link prediction task. |